class_docstr: Experience for Continual Reinforcement Learning purposes.
  
  The experience provides access to a `gym.Env` environment.
  Such environment can also be created lazily by
  providing a function.
code: def environment(self) -> Env:
      # support dynamic/lazy environment creation
      if not isinstance(self.env, Env):
          return self.env()
      return self.env
  
file_path: avalanche/avalanche/benchmarks/scenarios/reinforcement_learning.py
imports: from avalanche.benchmarks.scenarios import CLExperience, CLScenario, EagerCLStream
  from typing import Any, Callable, List, Optional, TypeVar, Union, Dict
  import numpy
  from avalanche.benchmarks.scenarios.generic_scenario import CLStream
label_desc: Use image node_id 2 for calling the RLExperience obj's underlying member method code with example usage: obj.environment() and returns: self, self